Intra-V1 functional networks and classification of observed stimuli
IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preproces...
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2024.1080173/full |
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| author | Marlis Ontivero-Ortega Marlis Ontivero-Ortega Marlis Ontivero-Ortega Jorge Iglesias-Fuster Jhoanna Perez-Hidalgo Daniele Marinazzo Mitchell Valdes-Sosa Mitchell Valdes-Sosa Pedro Valdes-Sosa Pedro Valdes-Sosa |
| author_facet | Marlis Ontivero-Ortega Marlis Ontivero-Ortega Marlis Ontivero-Ortega Jorge Iglesias-Fuster Jhoanna Perez-Hidalgo Daniele Marinazzo Mitchell Valdes-Sosa Mitchell Valdes-Sosa Pedro Valdes-Sosa Pedro Valdes-Sosa |
| author_sort | Marlis Ontivero-Ortega |
| collection | DOAJ |
| description | IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity.MethodsHere, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation.ResultsAll inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology.DiscussionOur findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization. |
| first_indexed | 2024-04-25T01:00:29Z |
| format | Article |
| id | doaj.art-5ec34268047b470581cffe8c26192693 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-25T01:00:29Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-5ec34268047b470581cffe8c261926932024-03-11T04:30:10ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962024-03-011810.3389/fninf.2024.10801731080173Intra-V1 functional networks and classification of observed stimuliMarlis Ontivero-Ortega0Marlis Ontivero-Ortega1Marlis Ontivero-Ortega2Jorge Iglesias-Fuster3Jhoanna Perez-Hidalgo4Daniele Marinazzo5Mitchell Valdes-Sosa6Mitchell Valdes-Sosa7Pedro Valdes-Sosa8Pedro Valdes-Sosa9The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, ChinaCuban Center for Neuroscience, Havana, CubaDepartment of Data Analysis, Ghent University, Ghent, BelgiumCuban Center for Neuroscience, Havana, CubaCuban Center for Neuroscience, Havana, CubaDepartment of Data Analysis, Ghent University, Ghent, BelgiumThe Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, ChinaCuban Center for Neuroscience, Havana, CubaThe Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, ChinaCuban Center for Neuroscience, Havana, CubaIntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity.MethodsHere, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation.ResultsAll inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology.DiscussionOur findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.https://www.frontiersin.org/articles/10.3389/fninf.2024.1080173/fullV1fMRIfunctional networksSVM-classifierNavon taskweight-maps |
| spellingShingle | Marlis Ontivero-Ortega Marlis Ontivero-Ortega Marlis Ontivero-Ortega Jorge Iglesias-Fuster Jhoanna Perez-Hidalgo Daniele Marinazzo Mitchell Valdes-Sosa Mitchell Valdes-Sosa Pedro Valdes-Sosa Pedro Valdes-Sosa Intra-V1 functional networks and classification of observed stimuli Frontiers in Neuroinformatics V1 fMRI functional networks SVM-classifier Navon task weight-maps |
| title | Intra-V1 functional networks and classification of observed stimuli |
| title_full | Intra-V1 functional networks and classification of observed stimuli |
| title_fullStr | Intra-V1 functional networks and classification of observed stimuli |
| title_full_unstemmed | Intra-V1 functional networks and classification of observed stimuli |
| title_short | Intra-V1 functional networks and classification of observed stimuli |
| title_sort | intra v1 functional networks and classification of observed stimuli |
| topic | V1 fMRI functional networks SVM-classifier Navon task weight-maps |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2024.1080173/full |
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